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Nonlinear Model Predictive Control: Theory and Algorithms

Nonlinear Model Predictive Control: Theory and Algorithms
Kataloginformation
Feldname Details
Vorliegende Sprache eng
Hinweise auf parallele Ausgaben 493530304 Druckausg. u.d.T.: ‡Grüne, Lars, 1967 - : Nonlinear model predictive control
ISBN 978-3-319-46023-9
Name Grüne, Lars
Pannek, Jürgen
ANZEIGE DER KETTE Pannek, Jürgen
T I T E L Nonlinear Model Predictive Control
Zusatz zum Titel Theory and Algorithms
Auflage 2nd ed. 2017
Verlagsort Cham
Verlag Springer
Erscheinungsjahr 2017
2017
Umfang Online-Ressource (XIV, 456 p. 80 illus., 22 illus. in color, online resource)
Reihe Communications and Control Engineering
Titelhinweis Druckausg. u.d.T.: ‡Grüne, Lars, 1967 - : Nonlinear model predictive control
Printed editionISBN: 978-3-319-46023-9
ISBN ISBN 978-3-319-46024-6
Klassifikation TJFM
TEC004000
*93-02
93B45
93C10
93C55
93C57
49N90
93D20
93B05
629.8
TJ212-225
SK 880
ZQ 5290
Kurzbeschreibung This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine-the core of any nonlinear model predictive controller-works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. This book (second edition) has been substantially rewritten, edited and updated to reflect the significant advances that have been made since the publication of its predecessor, including: • a new chapter on economic NMPC relaxing the assumption that the running cost penalizes the distance to a pre-defined equilibrium; • a new chapter on distributed NMPC discussing methods which facilitate the control of large-scale systems by splitting up the optimization into smaller subproblems; • an extended discussion of stability and performance using approximate updates rather than full optimization; • replacement of the pivotal sufficient condition for stability without stabilizing terminal conditions with a weaker alternative and inclusion of an alternative and much simpler proof in the analysis; and • further variations and extensions in response to suggestions from readers of the first edition. Though primarily aimed at academic researchers and practitioners working in control and optimization, the text is self-contained, featuring background material on infinite-horizon optimal control and Lyapunov stability theory that also makes it accessible for graduate students in control engineering and applied mathematics
2. Kurzbeschreibung Introduction -- Discrete-Time and Sampled-Data Systems -- Nonlinear Model Predictive Control -- Infinite-Horizon Optimal Control -- Stability and Suboptimality Using Stabilizing Constraints -- Stability and Suboptimality Without Stabilizing Constraints -- Feasibility and Robustness -- Economic Nonlinear Model Predictive Control -- Distributed Nonlinear Model Predictive Control -- Variants and Extensions -- Numerical Discretization -- Numerical Optimal Control of Nonlinear Systems -- Appendix: NMPC Software Supporting This Book
1. Schlagwortkette Prädiktive Regelung
Nichtlineare Regelung
SWB-Titel-Idn 480949948
Signatur Springer E-Book
Bemerkungen Elektronischer Volltext - Campuslizenz
Elektronische Adresse $uhttp://dx.doi.org/10.1007/978-3-319-46024-6
Internetseite / Link Volltext
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